AI Governance Decision Briefs are concise board-level briefings focused on the moments when AI governance becomes non-delegable.
They address the point at which governance is no longer assessed by intent, principles, or framework alignment — but by decision authority, accountability, and defensibility under audit, regulatory scrutiny, incidents, and liability exposure.
Each Decision Brief isolates a concrete governance failure point:
where accountability shifts from structures to people,
where committees stop absorbing responsibility,
and where governance must be decided, signed, and defended.
These briefings are not commentary, guidance, or opinion.
They are written for boards and senior decision-makers who operate at the accountability threshold — before governance is tested, and before it must be reconstructed.
Why AI risk does not start in 2026 – but already sits in your risk register. The EU AI Act becomes generally applicable in August 2026.Legally correct.From a risk perspective, dangerously misleading. For CROs, the relevant question is not when regulation applies –but when unmanaged AI use becomes an unquantifiable risk. And that point is already reached in …
Why AI governance fails at runtime – not in policy Most organizations believe they “have AI governance”. Policies exist.Principles exist.Frameworks exist. And yet, governance repeatedly collapses exactly when it is tested:during incidents, audits, escalations, and regulatory scrutiny. This is not a documentation gap.It is a runtime failure. AI governance does not fail …
Why the EU AI Act does not start to affect companies in 2026 – but now The EU AI Act becomes generally applicable on 2 August 2026. That statement is legally correct.And strategically misleading. Organizations that treat 2026 as the starting point for AI governance misunderstand not only the regulation, but the already …
Most organizations do not lack AI principles, policies, or frameworks. They lack systems that can govern AI under real operating conditions. AI governance does not fail in principle — it fails at runtime, when systems cannot see, control, evidence, or escalate under pressure. AI governance is rarely tested in documentation.It is …
Most organizations invest heavily in AI capabilities. Models, data, cloud infrastructure, and productivity gains are carefully budgeted and tracked. What is rarely budgeted is AI governance as operational, decision-critical infrastructure. This is not an implementation oversight.It is a structural governance gap. When governance is treated as an add-on rather than part …
